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The New Risk Manager (Part 2)

As we explained in our last post, the launch of Darwinex reloaded will bring important changes in the functionality of our Risk Manager.

How you already know at this stage, the Risk Manager is one of the fundamental pillars of Darwinex, controlling the investor risk in an independent manner from the trader and maintaining the same risk for all the DARWINS.

The technical challenge behind the Darwinex algorithm is important: it must function for whichever type of trading system and under all market conditions… and all this in the shortest time possible so that the slippage from when the trader opens his position, until it is replicated for the investors, is minimized.

Apart from the announced changes in the last post, Darwinex reloaded will be accompanied with an important change in the new Risk Manager.

Reference period to calculate the VaR in the underlying strategy

The risk manager basically makes two adjustments in the leverage of the investors vs. the traders. Depending on how the risk in the account behaves in a proportional manner to the leverage of the underlying trades, the Risk Manager will apply the first adjustment in function of the VaR of the underlying strategy, with respect to the objective VaR of the investors.

Investor leverage = Strategy leverage * VaR (target)/ VaR (strategy)

If the underlying strategy maintains a stable VaR over a long period, with just this first adjustment it would achieve the desired level of VaR for the investors.

However, the trader’s risk is usually unstable, thus it is necessary that we have a second adjustment, for the cases in which the trader over leverages outside of what he does habitually. We will explain this second adjustment in another post.

Centralising ourselves in the first formula (assuming the trader maintains his leverage in a stable manner), the leverage of the investors is very closely aligned with the calculation of the VaR of the underlying strategy.

For it’s calculation, obviously, we have to build on the trader’s operational history. The big dilemma is, how much trading history do we need as a reference? The answer is simple: it must take a period of time that would encompass the all the normal cycles of the strategy’s leverage. It sounds easy but the calculation is complex.

For example, we are going to analyse two different strategies.

Strategy 1

The first strategy illustrates consistency in the leverage, the number of monthly operations and the length of these operations. That is to say, taking the history of the last month of it’s operations, we would obtain a representative shot of what we would hope this trader would do in the following month.

The leverage cycles are short, therefore that VaR value would always be very similar, (independently of the history that we have taken as a reference for this calculation).

Strategy 2

Different to the previous strategy, the Strategy 2 causes more difficulties in choosing the reference timeframe for the VaR calculation because it has longer leverage cycles. The strategy, despite at first glance not appearing to be, does have discipline in his risk management, despite the leverage being very erratic. This strategy has long periods of similar leverage over the two months. In this period of time, it can be seen that the trader apparently had a defined maximum risk (leverage) that didn’t pass the approximate level of 30.

For this second case, if we took values of less than two months for the calculation, the VaR value would vary a lot. On the other hand, if we took values of longer than two months, the VaR would be much more stable, always taking into account the peaks of leverage of the user. The VaR obtained would be a lot higher but, at the same time, representative of how the strategy behaves.

From 21 to 45 days of reference

Up until now, the calculation of the VaR has considered a period of 21 days in that a trader has had open positions. That is to say for example, that if the trader operates in one day out of every two, it would be considered to be a 42 day history. This period of time has been seen to be insufficiently representative and because of that we have decided to raise that reference period to 45 days (more than double that of which we have been using).

The principal effects of this change are:

1) VaR curves a bit higher than normal, but more stable for every strategy. This can result in a lower profitability in some DARWINS.

2) DARWIN curves a lot more similar to the underlying strategy, which is principally what we are looking for with this change.

From this situation, the doubt arises as to why we do not pick a larger time period or why do we not adapt the selected time to the different trading styles.

The answer is that the greater the reference period, the greater the VaR would be and the less the sensitivity of the VaR curve would be to the behavioral changes of the underlying strategy.

Based on the different studies and analysis of our Quant team, 45 days is the optimum reference period to calculate the VaR without producing a significant loss of profitability in the DARWINS.

On the other hand, we have dismissed adapting the reference period of each type of trading style, because it would slow down the process of calculating the VaR (and as a consequence the function of the real time DARWIN Risk Manager). Besides, we understand that the criteria must be unique, so as not to avoid distinct results between different DARWINS.

A third post on the upcoming Risk Manager is underway! As ever, in info@dariwnex.com we are available for whatever doubt that you may have.

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I think it necessary to recall that the version of the calculation of the VaR on 21 traded days is not the ‘original’ version but a modification made in August 2016.

The earlier version was I think dynamically subservient according to the duration of the D-Periods.

My post below of October 2016 clearly indicates that this change has influenced the volatility of the strategies VaR, and consequently on the volatility of the Darwins.

At least I was wrong on one point: I did not preach in the desert :wink:

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Thanks for your remark, @Medialux.

This is not exactly correct, I am afraid. Before the changes introduced in August, the calculation of exposure was indeed changed to 45 trading days (which is approx. 3 D-Periods).

The period used for distribution of trade durations & D-Leverage distribution was (and still is) set at 21 trading days, though. In the coming weeks we’ll be changing the latter to 45 days, which is a major change compared to the changes made in August. [quote=“Medialux, post:2, topic:1419”]
My post below of October 2016 clearly indicates that this change has influenced the volatility of the strategies VaR, and consequently on the volatility of the Darwins.
[/quote]

The change in volatility that took place in October was NOT related to these changes. As you may know, D-Leverage is based on EURUSD volatility. The special trading conditions in October gave rise to a lower than usual EURUSD volatility, which had a direct impact on DARWIN replications.

This was solved by introducing a correction factor in the past. I am confident you’ll like the new Risk Manager!

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How does the risk manager handle strategies that itself adjust their leverage depending on current market volatility?
Winnetou is trading with a bigger stop-loss (lower leverage) during high volatility and with a tighter stop-loss in quiet markets (higher leverage). Is there a possibility that the risk manager could be overcompensating those changes, resulting in even further increased leverage during low market volatility and vice versa?

Thin blue line = Leverage per Trade
Grey area = inverted EUR/USD volatility (valley => high volatility)

(Timescale does not fit perfectly)

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Thanks for your message, @KlondikeFX

I’ll run this past our quants and revert to you.

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Quants confirm this is not exactly how it works. The lower the volatility, the lower the D-Leverage and hence the lower the chances that the risk manager’s risk adjustment takes place.

In case of high volatility, D-Leverage will be higher for the same leverage level, which means the risk manager could adjust your position due to the highest D.Leverage threshold being reached.

SL / TP are not relevant for this purpose. I hope this helps?

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Thanks a lot @ignacio that makes sense. I have just rewatched the videos about D-Leverage, VaR and Risk Management :slight_smile:

Can you quickly confirm that I got this right?

D-Leverage describes the current market exposure risk:

  • High D-Leverage in a volatile currency pair
  • High D-Leverage during volatile market conditions (Brexit vs. Pre-Asian Session)
  • High D-Leverage with multiple trades on correlating instruments

So you cannot completely control the D-Leverage yourself. The Risk Manager intervenes once the actual leverage of your trade(s) exceeds the upper band of the leverage target which is described by the strategy’s underlying VaR.

Now, let’s take an example on the EUR/USD. Given the market is in a very volatile stage and the trader reduces the leverage for his next trade from 10:1 to 5:1. The D-Leverage of the trade (before market volatility adjustments) would reduce from 10 to 5. As the trader’s leverage is not the only determining factor for D-Leverage, after market volatility adjustments the D-Leverage of 5 would be increased to 10, fitting the trade perfectly between the leverage bands :wink:

Let’s assume the trader had not changed his leverage, he would now sit with a D-Leverage of 15, making the Risk-Manager intervene and penalising his risk-management score? So it is actually good practice to reduce your leverage during volatile times as you will be rewarded by the Risk-Manager :slight_smile:

Are there still plans to provide some kind of risk-manager-api to request the “optimal” leverage for a trade?

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You git this right, @KlondikeFX!

We plan to work on a DARWIN Manager so you can access all the relevan info. Also, if you guys deem a D-Leverage API could be useful, this is sth we’d be keen on launching!

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Those are great ideas and obviously much awaited. Anything API is welcomed to attract developpers and make the end user experience broader.

Since everyone does not code or to the required level, a light ready-to-use application on the D-leverage derived from the Api would reach a wider audience, but if you do not take this plunge, either free sharings or commercial helpers will surely emerge.

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